With the proliferation of wireless infrastructure, smart controllers, and smart devices, numerous industries are relying on distributed machine-to-machine (M2M) environments and internet of things (IoT) for improving operational efficiency. Existing M2M deployments require numerous controllers and heterogeneous hardware/communication interfaces to interface the M2M sensors to their respective central units to receive, process and transmit data. In any physical environment, the deployed M2M/IoT platform collects structured and unstructured data and a variety of related and unrelated perpetual events which are processed by one or more nodes depending on its physical and functional capabilities.
The controllers and interfacing M2M devices usually have pre-fed software that caters to specified situations and where the runtime operates on a fixed set of rules/logic within its boundary of operations. Due to the constraints of processing and communication capacity, any single node with preloaded structural intelligence will have limited scope to correlate and aggregate raw events, develop situational awareness and take prompt actions to manage the disruptive operational environment of a M2M/IoT ecosystem. These systems have limited ability to leverage machine intelligence and collaborative learning within a specified system boundary. The cognition process in these systems is generally confined to data mining and based on limited pre-fed intelligence.
The complexities and uncertainties of the operational environment, the sensory nodes and edge computing devices demand a uniform platform with distributed intelligence which collaborates and organizes the process of analyzing, processing, and routing of data, events and commands based on changing context, exceptions, and serviceability of the operational environment. In order to support uncertain operational chaos and disruptive process management in real-time or near-real-time, the system needs dynamic execution pattern rather than relying on a static state machine implemented within interconnected intelligent nodes.
Accordingly, there is a significant need for a cognitive intelligent system that will not only monitor and process data and take actions in each known system environment but incrementally learn from its actions as well as the knowledge gained by its peers and supervisors and apply acquired knowledge to continually adjust the system to adapt and execute in any dynamic environment. It is therefore an object of the present invention to treat the physical environment as a whole, optimize the collaboration pattern, and allow mutual sharing of learning to solve problems in real-time or near real-time.